We employ the pretrained ResNet-50 [1]} as the encoder network. The decoder is consisted of five consecutive deconvolutional layers [2]} to restore the original resolution. Considering that haze distribution is non-homogeneous across spatial location, we aim to build the long-range interactions between different regions with similar structure. Meanwhile, it is critical to capture the relationship between channels. Inspired by the graph convolutional network, We develop Spatial Graph Reasoning module and Channel Graph Reasoning module to model the long-range dependency between regions and channels on a graph, respectively.
